From: AAAI Technical Report FS-02-02. Compilation copyright © 2002, AAAI (www.aaai.org). All rights reserved.
Trust in computer technology and the implications for
design and evaluation
John D. Lee and Katrina A. See
Department of Mechanical and Industrial Engineering
University of Iowa
Iowa City, IA 52242
Abstract
Computer technology is pervasive and often problematic,
and the sociological construct of trust has emerged as an
important intervening variable to describe reliance on
technology. Trust is an important mechanism for coping
with the cognitive complexity that accompanies increasingly
sophisticated technology. Because of this, understanding
trust can help explain reliance on technology and human
performance in complex systems. This review characterizes
trust by summarizing organizational, sociological,
interpersonal, and neurological perspectives.
These
perspectives provide a clear definition of trust, account of
how individual differences influence trust, and describe how
characteristics of technology affect trust. A conceptual
model integrates these findings and highlights the
importance of closed-loop dynamics, the role of context,
and the influence of display characteristics. This model has
important implications for how systems should be designed
to calibrate trust and encourage appropriate reliance.
Design and evaluation of technologically intensive systems
should consider the calibration of trust if they are to be
effective.
The growing cognitive complexity that
accompanies computer-mediated collaboration and multiagent automation makes understanding trust increasingly
important.
Introduction
Sophisticated information technology is becoming
ubiquitous, appearing in work environments as diverse as
aviation, maritime operations, process control, motor
vehicle operation, and information retrieval. Automation
is information technology that actively transforms data,
makes decisions, or controls processes. Automation
exhibits tremendous potential to extend human
performance and improve safety, however, recent disasters
indicate that it is not uniformly beneficial. In one case
pilots, trusting the ability of the autopilot, failed to
intervene and take manual control even as the autopilot
crashed an Airbus A320 (Sparaco, 1995). In another
instance, an automated navigation system malfunctioned
and the crew failed to intervene, allowing the Royal
Majesty cruise ship to drift off course for 24 hours before it
ran aground (Lee & Sanquist, 2000). On the other hand,
people are not always willing to trust in automation.
Operators rejected automated controllers in paper mills,
undermining the potential benefits of the automation
(Zuboff, 1988). As information technology becomes more
prevalent, poor partnerships between people and
automation will become increasingly costly and
catastrophic.
Misuse and disuse describe poor partnerships between
automation and people (Parasuraman & Riley, 1997).
Misuse refers to the failures that occur when people
inadvertently violate critical assumptions and rely on
automation inappropriately, whereas disuse signifies
failures that occur when people reject the capabilities of
automation. Misuse and disuse are two examples of
inappropriate reliance on automation that can compromise
safety and profitability in many situations. Understanding
how to mitigate disuse and misuse of automation is a
critically important problem with broad ramifications.
Understanding the role of trust in human-automation
relationships may help address misuse and disuse.
Several lines of recent research suggest that misuse and
disuse of technology depend on certain emotions and
attitudes of users, such as trust. In particular, many studies
show that humans respond socially to technology and treat
computers as they would other human collaborators. For
example, the similarity attraction hypothesis in social
psychology predicts people with similar personality
characteristics will be attracted to each other. This finding
also predicts user acceptance of software (Nass, Moon,
Fogg, Reeves, & Dryer, 1995; Reeves & Nass, 1996).
Software that displays personality characteristics similar to
those of the user tends to be more readily accepted.
Similarly, the concept of affective computing suggests that
computers that can sense and respond to user’s emotional
states may greatly improve human-computer interaction
(Norman, Ortony, & Russell, in press; Picard, 1997). In
considering emotion in the design of technology, designers
must recognize that techniques that increase acceptance or
trust do not necessarily improve performance. The critical
challenge is to calibrate trust to encourage appropriate
reliance.
Trust seems particularly important for understanding
human interaction with automation. More generally, the
construct of trust and credibility may also apply to
computer-mediated collaboration, where a person may
work with automation, multiple automated agents, or other
people through automated intermediaries. Trust and
credibility become important when computers play an
active and visible role in mediating how people interact
with data or the environment (Tseng & Fogg, 1999).
Considerable research has shown that trust is an important
attitude that mediates how people rely on each other
(Deutsch, 1958; Deutsch, 1969; Rempel, Holmes, &
Zanna, 1985; Ross & LaCroix, 1996; Rotter, 1967).
Sheridan has argued that just as trust mediates the
relationships between people; it may also mediate the
relationship between people and automation (Sheridan &
Hennessy, 1984).
A series of experiments have
demonstrated that trust is an attitude toward automation
that affects reliance, and that it can be measured
consistently (Lee & Moray, 1992; Lee & Moray, 1994;
Lewandowsky, Mundy, & Tan, 2000; Moray, Inagaki, &
Itoh, 2000; Muir & Moray, 1996).
Trust and Complexity: Organizational,
Sociological, and Interpersonal Perspectives
Researchers from a broad range of disciplines have
examined the role of trust in mediating relationships
between organizations, between individuals and
organizations, and between individuals. Specifically, trust
plays a critical role in interpersonal relationships, where
the focus is often on romantic relationships (Rempel et al.,
1985). Exchange relationships represent another important
research area, where the focus is trust between
management and employees and supervisors and
subordinates (Tan & Tan, 2000). Trust has an important
role in organizational productivity and strengthening
organizational commitment (Nyhan, 2000). Trust between
firms and customers has also become an important
consideration in the context of relationship management
(Morgan & Hunt, 1994) and internet commerce (Muller,
1996). Researchers have even considered the issue of trust
in the context of the relationship between organizations
such as multinational firms (Ring & Vandeven, 1992),
where cross-disciplinary and cross-cultural collaboration is
critical (Doney, Cannon, & Mullen, 1998). Interest in trust
has grown dramatically in the last five years as many have
come to recognize the importance of trust in promoting
efficient transactions and cooperation. Trust has emerged
as a central focus of organizational theory (Kramer, 1999),
has been the focus of a recent special issue of the Academy
of Management Review (Jones & George, 1998), a
workshop at Computer Human Interaction 2000 (Corritore,
Kracher, & Wiedenbeck, 2001), and a book (Kramer &
Tyler, 1996).
The general theme of the increasing cognitive complexity
of work explains the recent interest in trust. Trust tends to
be less important in well-structured, stable environments
such as procedure-based hierarchical organizations, where
an emphasis on order and stability minimize transactional
uncertainty (Moorman, Deshpande, & Zaltman, 1993).
Many organizations, however, have recently adopted agile
structures, self-directed work groups, and complex
automation, all of which make the workplace increasingly
complex, unstable, and uncertain. Because these changes
enable rapid adaptation to change and accommodate
unanticipated variability, there is a trend away from wellstructured, procedure-based environments. These changes
have the potential to make organizations and individuals
more productive, but they also increase cognitive
complexity and leave more degrees of freedom to the
person to resolve. Trust plays a critical role in people’s
ability to accommodate the cognitive complexity and
uncertainty that accompanies these changes.
Trust helps accommodate complexity in several ways. It
supplants supervision when direct observation becomes
impractical, and facilitates choice under uncertainty by
acting as a social decision heuristic (Kramer, 1999). It also
reduces uncertainty in estimating the response of others to
guide appropriate reliance and generate a collaborative
advantage (Baba, 1999; Ostrom, 1998). Moreover, trust
facilitates decentralization and adaptive behavior. The
increased complexity and uncertainty behind the increased
interest in trust in other fields parallels the increased
complexity and sophistication of automation. Trust in
automation guides effective reliance when the complexity
of the automation makes a complete understanding
impractical and when the situation demands adaptive
behavior that cannot be guided by procedures. For this
reason, the recent research on trust provides a rich
theoretical base for understanding reliance on complex
automation and, more generally, how it affects computermediated collaboration that involves both human and
computer agents.
The Dynamics of Trust
Figure 1 summarizes the factors affecting trust and the role
of trust in mediating reliance. The grey line that links the
level of trust to the performance of the automation
highlights calibration of trust as a central consideration for
guiding appropriate reliance. There are four critical
elements in this framework: the relationship between trust
and reliance, the closed-loop dynamics of trust and
reliance, the importance of context in mediating trust and
its effect on reliance, and the role of information display on
developing appropriate trust. The research addressing trust
between people provides a basis for elaborating this
framework to understand the relationship between trust and
reliance.
Trust and reliance: Belief, attitude, intention and
behavior
Understanding the nature of trust depends on defining the
relationship between dispositions, beliefs, attitudes,
intentions, and behaviors. These distinctions are of great
theoretical importance because multiple factors mediate the
process of translating an attitude into a behavior.
Considering trust as a behavior can generate confusion by
attributing effects to trust that are due to other factors
affecting intention or reliance (Ajzen & Fishbein, 1980;
Fishbein & Ajzen, 1975). An approach that keeps
attitudes, intention, and behavior conceptually distinct
provides a strong theoretical framework for understanding
the role of trust in automation.
According to this framework, beliefs represent the
information base that determines attitudes. An attitude is
an affective evaluation of beliefs that serves as a general
predisposition to a set of intentions. Intentions then
translate into behavior according to the environmental and
cognitive constraints a person faces. According to this
logic, definitions of trust should focus on the attitude rather
than the beliefs that underlie trust or the intentions or
behaviors that might result from different levels of trust.
Considering trust as an intention or behavior has the
potential to confuse the effect of trust on behavior with
other factors such as workload, situation awareness, and
self-confidence of the operator (Lee & Moray, 1994; Riley,
1989). No direct relationship between trust and intention
or behavior exists because other psychological and
environmental constraints intervene. Trust is an attitude
that influences intention and behavior. Figure 1 shows that
trust stands between beliefs about the characteristics of the
automation and the intention to rely on the automation.
Individual and Environmental Context
Factors affecting
automation performance
Workload
Effort to engage
Self-confidence
Reputation
Interface features
Time constraints
Configuration errors
Predisposition to trust
Information assimilation
and Belief formation
Knowledge-based
Rule-based
Skill-based
Trust
Intention
Reliance
Appropriateness of trust
Calibration
Resolution
Temporal specificity
Functional specificity
Automation
Display
Information about the automation
Attributional abstraction (purpose, process and performance)
Detail (system, function, sub-function, mode)
Figure 1. A conceptual model of the dynamic process that governs trust and its effect on reliance.
The cognitive processes associated with trust
The information that forms the basis of trust can be
assimilated in several qualitatively different ways. Some
argue that trust depends on a calculative process; others
argue that trust depends on the application of rules. Most
importantly, trust also seems to depend on an affective
response to the violation or confirmation of implicit
expectancies. The affective and calculative aspects of trust
depend on the evolution of the relationship between the
trustor and trustee, the information available to the trustor,
and the way the information is displayed.
A dominant approach to trust in the organizational sciences
considers trust from a rational choice perspective (Hardin,
1992). This view considers trust as an evaluation made
under uncertainty in which decision makers use their
knowledge of the motivations and interests of the other
party to maximize gains and minimize losses. Individuals
are assumed to make choices based on a rationally derived
assessment of costs and benefits (Lewicki & Bunker,
1996). Williamson (1993) argues that trust is primarily
calculative in business and commercial relations and that
trust represents one aspect of rational risk analysis. Trust
accumulates and dissipates based on the cumulative
interactions with the other agent. These interactions lead
to an accumulation of knowledge that establishes trust
through an expected value calculation, where the
probability of various outcomes is estimated with
increasing experience (Holmes, 1991).
Trust can also develop according to rules that link levels of
trust to characteristics of the agent. These can develop
through direct observation of the trusted agent,
intermediaries that convey their observations, and
presumptions based on standards, category membership,
and procedures (Kramer, 1999). Trust may depend on the
application of rules rather than on conscious calculations.
Explicit and implicit rules frequently guide individual and
collective collaborative behavior (Mishra, 1996). Sitkin
and Roth (1993) illustrate the importance of rules in their
distinction between trust as an institutional arrangement
and interpersonal trust. Institutional trust reflects the use
of contracts, legal procedures, and formal rules, whereas
interpersonal trust depends on shared values.
The
application of rules in institution-based trust reflects
guarantees and safety nets based on the organizational
constraints of regulations, guarantees, and legal recourse
that constrain behavior and make it more possible to trust
(McKnight, Cummings, & Chervany, 1998). Institutionbased trust increases people’s expectation of satisfactory
performance during times of normal operation by pairing
situations with rule-based expectations.
Intermediaries can also convey information to support
judgments of trust (Baba, 1999). Similarly, trust can be
based on presumptions of the trustworthiness of a category
or role of the agent rather than on the actual performance
of the agent (Kramer, 1999). If trust depends on rules that
characterize performance during normal situations, then
abnormal situations can lead trust to collapse because the
assurances associated with normal or customary operation
are no longer valid. Because of this, role and categorybased trust can be fragile, particularly when abnormal
situations disrupt otherwise stable roles.
Calculative and rule-based processes do not account for the
affective characteristics of trust, and fail to consider the
social and affective aspects of trust (Kramer, 1999).
Emotional responses are critical because people not only
think about trust, they also feel it (Fine & Holyfield, 1996).
Emotions fluctuate over time according to the performance
of the other agent, and they can signal instances where
expectations do not conform to the ongoing experience.
As trust is betrayed, emotions provide signals concerning
the changing nature of the situation (Jones & George,
1998).
Berscheid (1994) uses the concept of automatic processing
to describe how events that are relevant to affective trait
constructs, such as trust, are likely to be perceived even
under attentional overload situations. Because of this,
people process observations of new behavior according to
these constructs without always being aware of doing so.
Automatic processing plays a substantial role in
attributional activities, with many aspects of causal
reasoning occurring outside conscious awareness. In this
way, emotions bridge the gaps in rationality and enable
people to focus their limited attentional capacity (JohnsonLaird & Oately, 1992). Because the cognitive complexity
of relationships can exceed a person’s capacity to form a
complete mental model, people cannot perfectly predict
behavior, and emotions serve to redistribute cognitive
resources, manage priorities, and make decisions with
imperfect information (Johnson-Laird & Oately, 1992).
This is particularly critical when rules fail to apply and
when cognitive resources are not available to support a
calculated rational choice. The affective nature of trust
seems to parallel some of the features of skill-based
behavior, where an internalized dynamic world model
guides behavior without conscious intervention
(Rasmussen, 1983).
Recent neurological evidence parallels these findings,
indicating that emotions play an important role in decisionmaking. This research is important because it suggests a
neurologically-based description for how trust might
influence reliance. Damasio (1990) showed that although
people with brain lesions in the ventromedial sector of the
prefrontal cortices retain reasoning and other cognitive
abilities, their emotions and decision-making ability are
critically impaired. In a simple gambling decision-making
task, those with prefrontal lesions performed much worse
than a control group of normal people. Patients responded
to immediate prospects and failed to accommodate longterm consequences (Bechara, Damasio, Damasio, &
Anderson, 1994). In a subsequent study, normal subjects
showed a substantial response to a large loss, as measured
by skin conductance response (SCR), a physiological
measure of emotion, whereas patients did not (Bechara,
Damasio, Tranel, & Damasio, 1997). Interestingly, normal
subjects also demonstrated an anticipatory SCR and began
to avoid risky choices before they explicitly recognized the
alternative as being risky. These results strongly support
the argument that emotions play an important role in
decision-making, and that emotional reactions may
mediate trust without conscious awareness.
Emotion influences not only individual decision making
but also the decision making of groups, suggesting that
affect-based trust may play an important role in governing
reliance on automation. Emotion plays an important role
in decision making by supporting implicit communication
between several people (Pally, 1998). The tone, rhythm
and quality of speech convey information about the
emotional states of individuals, and so regulate the
physiological emotional responses of everyone involved.
People spontaneously match non-verbal cues to generate
emotional attunement. Emotional, non-verbal exchanges
are a critical complement to the analytic information in a
verbal exchange (Pally, 1998). These neuropsychological
results seem consistent with recent findings regarding
interpersonal trust in which anonymous computerized
messages were found to be not as effective as face-to-face
communication because individuals judge trustworthiness
from facial expressions and from hearing the way others
talk (Rocco, 1998). These results suggest that trust
calibration could be aided by incorporating a rich array of
informal cues into the computer interface. These cues
might begin to replicate the emotional attunement of faceto-face interactions between people.
Results from many studies show that develops and
influences behavior in more than one way. Trust between
individuals and in automation likely depends on a complex
interaction of knowledge-based calculations, rule-based
categorizations, and most importantly, the implicit skillbased interpretation of information.
The dynamic process of trust and reliance
Figure 1 shows that trust and its effect on reliance are part
of a closed-loop process, where the dynamic interaction
with the automation governs trust. If the system is not
trusted, it is unlikely to be used; if it is not used, the person
will have limited information regarding its capabilities and
it is unlikely that trust will grow. Because trust is largely
based on the observation of the automation, automation
must be relied upon for trust to grow (Muir & Moray,
1996). Relying on automation provides operators with an
opportunity to observe the automation and thus to develop
increasingly greater levels of trust.
The dynamic interplay between changes in trust, the effect
of these changes on reliance, and the resulting feedback
can generate substantial non-linear effects. The non-linear
cascade of effects noted in the dynamics of interpersonal
trust (Ostrom, 1997) and the high degree of volatility in the
level of trust in some relationships suggests a similar
phenomena occurs between people (McKnight et al.,
1998). This non-linear behavior may also account for the
large individual differences noted in human-automation
trust (Lee & Moray, 1994; Moray et al., 2000). A small
difference in the predisposition to trust may have a
substantial effect when it influences an initial decision to
engage the automation. A small difference in the initial
level of trust might, for example, lead one person to engage
the automation and another to adopt manual control.
Reliance on the automation may then lead to a substantial
increase in trust, whereas the trust of the person who
adopted manual control might decline. Characterizing the
dynamics of the feedback loop shown in Figure 1 is
essential to understanding why trust sometimes appears
volatile and other times stable.
This closed-loop
framework also highlights the importance of temporal
specificity in characterizing the appropriateness of trust.
The higher the temporal specificity, the more likely that
trust will correspond to the current capability of the
automaton. Poor temporal specificity will act as a time
delay, undermining system stability and increasing the
volatility of trust. The closed loop feedback may explain
how operator, environment, and automation characteristics
combine over time to affect trust and reliance.
Context and trust
Figure 1 shows that trust is an intervening variable that
indirectly influences intention and reliance.
Beliefs
combine with individual differences, such as the
predisposition to trust, to determine the level of trust in the
automation. Predisposition to trust is most important when
a situation is ambiguous and generalized expectancies
dominate, and becomes less important as the relationship
progresses (McKnight et al., 1998). The level of trust
combines with other attitudes and expectations such as
subjective workload and self-confidence to determine the
intention to rely on the automation. Self-confidence is
particularly critical factor in decision making in general
(Bandura, 1982; Gist & Mitchell, 1992) and in mediating
the effect of trust on reliance in particular (Lee & Moray,
1994). A variety of system and human performance
constraints affect how the intention to rely on automation
translates into actual reliance (Kirlik, 1993). For example,
the operator may intend to use the automation but not have
sufficient time to engage it. Understanding the role of trust
in the decision to rely on automation requires a
consideration of the operating context that goes beyond
trust alone.
The conceptual model in Figure 1 helps integrate the
research from psychology, sociology, and management
science. Most importantly, it shows that trust depends on
the dynamic interaction between operator, environment,
and automation characteristics.
Conclusions
A substantial research base from several disciplines
suggests that trust is an important mechanism to cope with
the increasing cognitive complexity associated with new
technology. In particular, research shows that trust
mediates reliance on automation. This review describes a
conceptual model of trust that can guide research and
design. Specific conclusions include:
•
•
Trust influences reliance as an attitude in the
context of beliefs, attitudes, intentions, and
behavior. Reliance is not a good measure of trust.
Designers should focus on calibrating trust not
necessarily enhancing trust. Maximizing trust is
often a poor design goal.
•
•
•
•
Trust depends on information assimilated through
calculative-, rule-, and skill-based processes.
Calibrating trust requires interface design that
considers qualitatively different cognitive
processes that influence trust.
Dynamic relationship between person and
automation
helps
explain
non-linear
characteristics of trust and reliance. Trust and
reliance may evolve over time in highly variable
and counterintuitive ways.
Interface design should capitalize on people’s
ability to process affective stimuli. Interface
design should incorporate subtle cues that guide
interpersonal responses, such as auditory cues
associated with verbal inflections, sighs, and
pauses to indicate uncertainty.
Systems that mimic human characteristics, may
inadvertently lead to inappropriate levels of trust
as the human characteristics lead to false
expectations. Human-like features should not be
arbitrarily incorporated into a design, but should
be carefully designed to calibrate trust.
These general points provide important considerations for
how systems might be designed and evaluated to enhance
trust calibration.
Acknowledgements
This work was supported by a grant from the NSF, IIS 0117494.
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